Systems and methods for interpolation with resolution preservation

ABSTRACT

Various methods and systems are provided for artifact reduction with resolution preservation. In one example, a method includes obtaining projection data of an imaging subject, identifying a metal-containing region in the projection data, interpolating the metal-containing region to generate interpolated projection data, extracting high frequency content information from the projection data in the metal-containing region, adding the extracted high frequency content information to the interpolated projection data to generate adjusted projection data, and reconstructing one or more diagnostic images from the adjusted projection data.

FIELD

This disclosure relates generally to diagnostic imaging and, moreparticularly, to reducing artifacts due to high-density objects incomputed tomography (CT).

BACKGROUND

Typically, in computed tomography (CT) imaging systems, an x-ray sourceemits a beam of x-ray radiation toward a subject or object, such as apatient or a piece of luggage. The beam, after being attenuated by thesubject, impinges upon an array of radiation detectors. The intensity ofthe attenuated beam radiation received at the detector array istypically dependent upon the attenuation of the x-ray radiation beam bythe subject. Each detector element of the detector array produces aseparate electrical signal indicative of the attenuated beam received byeach detector element. The electrical signals are transmitted to a dataprocessing system for analysis which ultimately produces an image.

Objects with high x-ray absorption properties (e.g., metal) can causeartifacts in reconstructed CT images, often resulting in images havinglow or non-diagnostic image quality. For example, metal implants such asamalgam dental fillings, joint replacements (e.g., plates and/or pinsused in hips, knees, shoulders, etc.), surgical clips, biopsy needles,or other hardware may generate streak or starburst artifacts in theformation of such images. Such artifacts typically result from a sharpdifference in signal attenuation at the boundary of the metal implantsand a patient's anatomy.

BRIEF DESCRIPTION

In one embodiment, a method includes obtaining projection data of animaging subject, identifying a metal-containing region in the projectiondata, interpolating the metal-containing region to generate interpolatedprojection data, extracting high frequency content information from theprojection data in the metal-containing region, adding the extractedhigh frequency content information to the interpolated projection datato generate adjusted projection data, and reconstructing one or morediagnostic images from the adjusted projection data.

It should be understood that the brief description above is provided tointroduce in simplified form a selection of concepts that are furtherdescribed in the detailed description. It is not meant to identify keyor essential features of the claimed subject matter, the scope of whichis defined uniquely by the claims that follow the detailed description.Furthermore, the claimed subject matter is not limited toimplementations that solve any disadvantages noted above or in any partof this disclosure.

BRIEF DESCRIPTION OF THE DRAWINGS

The present disclosure will be better understood from reading thefollowing description of non-limiting embodiments, with reference to theattached drawings, wherein below:

FIG. 1 shows a pictorial view of an imaging system, according to anembodiment;

FIG. 2 shows a block schematic diagram of an exemplary imaging system,according to an embodiment;

FIG. 3 shows a flow chart illustrating an example method for metalartifact reduction with resolution preservation;

FIG. 4 shows an example sequence of images representing various aspectsof the method of FIG. 3 ; and

FIGS. 5 and 6 show example images reconstructed from projection data,according to examples of the present disclosure.

DETAILED DESCRIPTION

The present disclosure is directed to systems and methods for addressingimaging artifacts, such as metal artifacts, while preserving imageresolution in diagnostic medical images. High density objects such asmetal present in the body of patients can cause artifacts that mayhinder a diagnosis. These artifacts may be caused due to several factorssuch as beam-hardening, scatter, photon starvation, partial volume,aliasing, and under-range in the data acquisition, as examples. Theartifacts may be reduced by metal artifact reduction (MAR) techniques.For instance, advanced beam-hardening, noise reduction, and scattercorrection have been proposed for standard filtered back-projectionalgorithm. In another example, an iterative reconstruction algorithm forMAR incorporates the shape of the metal and may use a polyenergeticmodel to reduce beam-hardening artifacts. These algorithms belong to acategory generally referred to as model-based algorithms.

In addition to the category of model-based algorithms, another class ofalgorithms can be described as “sinogram in-painting.” In this class ofalgorithms, the sinogram data that is corrupted by the high-densityobject is discarded and replaced by either data interpolated usingneighboring projections or data estimated by solving a cost functionusing some iterative algorithms, or by a forward projection of a priorimage. These methods can be single steps or can involve successiverefinements via an iterative process.

Pure interpolation techniques, however, may create additional artifactsin the reconstructed image due to inconsistency in the data, and thus atwo-pass technique may be applied that includes a first step ofcorrection using an interpolation technique and the resultant first passimage is then used to generate a prior image. During the second pass thecorrupted data is replaced by the forward projection of the prior imageto generate the in-painted sinogram.

In a typical CT acquisition a prior image is not available and hence maybe generated using corrupted data. In an ideal sense, the prior imageshould include any knowledge of the object without the high-densityartifacts. Availability of an atlas, and then registering the images tothe atlas, can in principle lead to a prior image. In practice, theprior image is typically generated by performing a non-lineartransformation on either the original image or a first pass MAR image.However, it is often challenging to use the original image in thepresence of metal (or high density) artifacts, and a first-pass MARimage typically has degraded information content close to the metal,thus the first-pass MAR image is not consistent with the original image,and non-linear processing will not add content to the prior image.

In another approach, the in-painted sinogram data can be combined withthe original data to generate projection data which can then be fed to areconstruction algorithm. This approach is general enough and can beused in conjunction with in-painted data generated using any of themethods described above. However, these in-painting techniques generallyresult in loss of resolution near the metal or high-density artifact.

Thus, according to embodiments disclosed herein, the image resolution inthe vicinity of a metal artifact or other high-density artifact may bemaintained while addressing the metal or high-density artifact. Toaccomplish the resolution preservation while addressing the high densityartifact, the high frequency content of the projection data in theidentified metal/high-density region is extracted and conditioned andadded back to the interpolated projection data to preserve resolutionand enable artifact reduction in the image domain.

The metal artifact reduction method described herein may includeinitially reconstructing projection data obtained via a CT imagingsystem, such as the CT system of FIGS. 1 and 2 , to generate an initialimage in order to identify where metal or another high-density object islocated. The identified metal in the initial image may be mapped back tothe view (projection) domain. Any place where metal is located in theprojection data may determined to be corrupted. Thus, the methoddescribed herein, such as the method shown in FIG. 3 , interpolates overa metal region to remove the corrupted data and replace the data withthe interpolated data. However, interpolation causes loss of highfrequency information, which causes blurring and other residualartifacts. To counteract this blurring and other residual artifacts, themetal artifact reduction method described herein extracts the highfrequency information in the metal-containing region of the original(e.g., non-interpolated) projection data and then adds the highfrequency information back in to the interpolated projection data, asschematically represented by the sequence of images shown in FIG. 4 .Further, the high frequency content is conditioned prior to adding thehigh frequency content to the interpolated data to avoid streaking. Forexample, the edges of a metal object may include a relatively highamount of high frequency content and including an excess of highfrequency content (e.g., relative to the middle of the metal object) mayintroduce streaking artifacts. Additionally, the high frequency contentmay include noise, and the conditioning may discard this noise. Theconditioning may include weighting, thresholding, or filtration, such asdifferent weighting schemes or thresholding to de-weight at the edge ofthe metal. In some examples, dilation and/or erosion may be used toweigh which frequency content to include. The high frequency informationmay be extracted using a 2-D or 3-D filter on each view to smooth dataout, and the smoothed image may be subtracted from the original toextract the high frequency content. Once the high frequency informationis extracted, conditioned, and added to the interpolated projectiondata, the interpolated projection data (with the high frequency content)may be reconstructed into one or more diagnostic images, examples ofwhich are shown in FIGS. 5 and 6 .

FIG. 1 illustrates an exemplary CT system 100 configured for CT imaging.Particularly, the CT system 100 is configured to image a subject 112such as a patient, an inanimate object, one or more manufactured parts,and/or foreign objects such as dental implants, stents, and/or contrastagents present within the body. In one embodiment, the CT system 100includes a gantry 102, which in turn, may further include at least onex-ray source 104 configured to project a beam of x-ray radiation 106(see FIG. 2 ) for use in imaging the subject 112 laying on a table 114.Specifically, the x-ray source 104 is configured to project the x-rayradiation beams 106 towards a detector array 108 positioned on theopposite side of the gantry 102. Although FIG. 1 depicts only a singlex-ray source 104, in certain embodiments, multiple x-ray sources anddetectors may be employed to project a plurality of x-ray radiationbeams 106 for acquiring projection data at different energy levelscorresponding to the patient. In some embodiments, the x-ray source 104may enable dual-energy gemstone spectral imaging (GSI) by rapid peakkilovoltage (kVp) switching. In some embodiments, the x-ray detectoremployed is a photon-counting detector which is capable ofdifferentiating x-ray photons of different energies. In otherembodiments, two sets of x-ray sources and detectors are used togenerate dual-energy projections, with one set at low-kVp and the otherat high-kVp. It should thus be appreciated that the methods describedherein may be implemented with single energy acquisition techniques aswell as dual energy acquisition techniques.

In certain embodiments, the CT system 100 further includes an imageprocessor unit 110 configured to reconstruct images of a target volumeof the subject 112 using an iterative or analytic image reconstructionmethod. For example, the image processor unit 110 may use an analyticimage reconstruction approach such as filtered back projection (FBP) toreconstruct images of a target volume of the patient. As anotherexample, the image processor unit 110 may use an iterative imagereconstruction approach such as advanced statistical iterativereconstruction (ASIR), conjugate gradient (CG), maximum likelihoodexpectation maximization (MLEM), model-based iterative reconstruction(MBIR), and so on to reconstruct images of a target volume of thesubject 112. As described further herein, in some examples the imageprocessor unit 110 may use both an analytic image reconstructionapproach such as FBP in addition to an iterative image reconstructionapproach.

In some CT imaging system configurations, an x-ray source projects acone-shaped x-ray radiation beam which is collimated to lie within anX-Y-Z plane of a Cartesian coordinate system and generally referred toas an “imaging plane.” The x-ray radiation beam passes through an objectbeing imaged, such as the patient or subject. The x-ray radiation beam,after being attenuated by the object, impinges upon an array of detectorelements. The intensity of the attenuated x-ray radiation beam receivedat the detector array is dependent upon the attenuation of a radiationbeam by the object. Each detector element of the array produces aseparate electrical signal that is a measurement of the x-ray beamattenuation at the detector location. The attenuation measurements fromall the detector elements are acquired separately to produce atransmission profile.

In some CT systems, the x-ray source and the detector array are rotatedwith a gantry within the imaging plane and around the object to beimaged such that an angle at which the radiation beam intersects theobject constantly changes. A group of x-ray radiation attenuationmeasurements, e.g., projection data, from the detector array at onegantry angle is referred to as a “view.” A “scan” of the object includesa set of views made at different gantry angles, or view angles, duringone revolution of the x-ray source and detector. It is contemplated thatthe benefits of the methods described herein accrue to medical imagingmodalities other than CT, so as used herein the term “view” is notlimited to the use as described above with respect to projection datafrom one gantry angle. The term “view” is used to mean one dataacquisition whenever there are multiple data acquisitions from differentangles, whether from a CT, positron emission tomography (PET), orsingle-photon emission CT (SPECT) acquisition, and/or any other modalityincluding modalities yet to be developed as well as combinations thereofin fused embodiments.

The projection data is processed to reconstruct an image thatcorresponds to a two-dimensional slice taken through the object or, insome examples where the projection data includes multiple views orscans, a three-dimensional rendering of the object. One method forreconstructing an image from a set of projection data is referred to inthe art as the filtered back projection technique. Transmission andemission tomography reconstruction techniques also include statisticaliterative methods such as maximum likelihood expectation maximization(MLEM) and ordered-subsets expectation-reconstruction techniques as wellas iterative reconstruction techniques. This process converts theattenuation measurements from a scan into integers called “CT numbers”or “Hounsfield units,” which are used to control the brightness of acorresponding pixel on a display device.

To reduce the total scan time, a “helical” scan may be performed. Toperform a “helical” scan, the patient is moved while the data for theprescribed number of slices is acquired. Such a system generates asingle helix from a cone beam helical scan. The helix mapped out by thecone beam yields projection data from which images in each prescribedslice may be reconstructed.

As used herein, the phrase “reconstructing an image” is not intended toexclude embodiments of the present invention in which data representingan image is generated but a viewable image is not. Therefore, as usedherein, the term “image” broadly refers to both viewable images and datarepresenting a viewable image. However, many embodiments generate (orare configured to generate) at least one viewable image.

FIG. 2 illustrates an exemplary imaging system 200 similar to the CTsystem 100 of FIG. 1 . In accordance with aspects of the presentdisclosure, the imaging system 200 is configured for imaging a subject204 (e.g., the subject 112 of FIG. 1 ). In one embodiment, the imagingsystem 200 includes the detector array 108 (see FIG. 1 ). The detectorarray 108 further includes a plurality of detector elements 202 thattogether sense the x-ray radiation beam 106 (see FIG. 2 ) that passthrough the subject 204 (such as a patient) to acquire correspondingprojection data. Accordingly, in one embodiment, the detector array 108is fabricated in a multi-slice configuration including the plurality ofrows of cells or detector elements 202. In such a configuration, one ormore additional rows of the detector elements 202 are arranged in aparallel configuration for acquiring the projection data.

In certain embodiments, the imaging system 200 is configured to traversedifferent angular positions around the subject 204 for acquiring desiredprojection data. Accordingly, the gantry 102 and the components mountedthereon may be configured to rotate about a center of rotation 206 foracquiring the projection data, for example, at different energy levels.Alternatively, in embodiments where a projection angle relative to thesubject 204 varies as a function of time, the mounted components may beconfigured to move along a general curve rather than along a segment ofa circle.

As the x-ray source 104 and the detector array 108 rotate, the detectorarray 108 collects data of the attenuated x-ray beams. The datacollected by the detector array 108 undergoes pre-processing andcalibration to condition the data to represent the line integrals of theattenuation coefficients of the scanned subject 204. The processed dataare commonly called projections.

In some examples, the individual detectors or detector elements 202 ofthe detector array 108 may include photon-counting detectors whichregister the interactions of individual photons into one or more energybins. It should be appreciated that the methods described herein mayalso be implemented with energy-integrating detectors.

The acquired sets of projection data may be used for basis materialdecomposition (BMD). During BMD, the measured projections are convertedto a set of material-density projections. The material-densityprojections may be reconstructed to form a pair or a set ofmaterial-density map or image of each respective basis material, such asbone, soft tissue, and/or contrast agent maps. The density maps orimages may be, in turn, associated to form a volume rendering of thebasis material, for example, bone, soft tissue, and/or contrast agent,in the imaged volume.

Once reconstructed, the basis material image produced by the imagingsystem 200 reveals internal features of the subject 204, expressed inthe densities of two basis materials. The density image may be displayedto show these features. In traditional approaches to diagnosis ofmedical conditions, such as disease states, and more generally ofmedical events, a radiologist or physician would consider a hard copy ordisplay of the density image to discern characteristic features ofinterest. Such features might include lesions, sizes and shapes ofparticular anatomies or organs, and other features that would bediscernable in the image based upon the skill and knowledge of theindividual practitioner.

In one embodiment, the imaging system 200 includes a control mechanism208 to control movement of the components such as rotation of the gantry102 and the operation of the x-ray source 104. In certain embodiments,the control mechanism 208 further includes an x-ray controller 210configured to provide power and timing signals to the x-ray source 104.Additionally, the control mechanism 208 includes a gantry motorcontroller 212 configured to control a rotational speed and/or positionof the gantry 102 based on imaging requirements.

In certain embodiments, the control mechanism 208 further includes adata acquisition system (DAS) 214 configured to sample analog datareceived from the detector elements 202 and convert the analog data todigital signals for subsequent processing. The DAS 214 may be furtherconfigured to selectively aggregate analog data from a subset of thedetector elements 202 into so-called macro-detectors, as describedfurther herein. The data sampled and digitized by the DAS 214 istransmitted to a computer or computing device 216. In one example, thecomputing device 216 stores the data in a storage device or mass storage218. The storage device 218, for example, may include a hard disk drive,a floppy disk drive, a compact disk-read/write (CD-R/W) drive, a DigitalVersatile Disc (DVD) drive, a flash drive, and/or a solid-state storagedrive.

Additionally, the computing device 216 provides commands and parametersto one or more of the DAS 214, the x-ray controller 210, and the gantrymotor controller 212 for controlling system operations such as dataacquisition and/or processing. In certain embodiments, the computingdevice 216 controls system operations based on operator input. Thecomputing device 216 receives the operator input, for example, includingcommands and/or scanning parameters via an operator console 220operatively coupled to the computing device 216. The operator console220 may include a keyboard (not shown) or a touchscreen to allow theoperator to specify the commands and/or scanning parameters.

Although FIG. 2 illustrates only one operator console 220, more than oneoperator console may be coupled to the imaging system 200, for example,for inputting or outputting system parameters, requesting examinations,plotting data, and/or viewing images. Further, in certain embodiments,the imaging system 200 may be coupled to multiple displays, printers,workstations, and/or similar devices located either locally or remotely,for example, within an institution or hospital, or in an entirelydifferent location via one or more configurable wired and/or wirelessnetworks such as the Internet and/or virtual private networks, wirelesstelephone networks, wireless local area networks, wired local areanetworks, wireless wide area networks, wired wide area networks, etc.

In one embodiment, for example, the imaging system 200 either includes,or is coupled to, a picture archiving and communications system (PACS)224. In an exemplary implementation, the PACS 224 is further coupled toa remote system such as a radiology department information system,hospital information system, and/or to an internal or external network(not shown) to allow operators at different locations to supply commandsand parameters and/or gain access to the image data.

The computing device 216 uses the operator-supplied and/orsystem-defined commands and parameters to operate a table motorcontroller 226, which in turn, may control a table 114 which may be amotorized table. Specifically, the table motor controller 226 may movethe table 114 for appropriately positioning the subject 204 in thegantry 102 for acquiring projection data corresponding to the targetvolume of the subject 204.

As previously noted, the DAS 214 samples and digitizes the projectiondata acquired by the detector elements 202. Subsequently, an imagereconstructor 230 uses the sampled and digitized x-ray data to performhigh-speed reconstruction. Although FIG. 2 illustrates the imagereconstructor 230 as a separate entity, in certain embodiments, theimage reconstructor 230 may form part of the computing device 216.Alternatively, the image reconstructor 230 may be absent from theimaging system 200 and instead the computing device 216 may perform oneor more functions of the image reconstructor 230. Moreover, the imagereconstructor 230 may be located locally or remotely, and may beoperatively connected to the imaging system 200 using a wired orwireless network. Particularly, one exemplary embodiment may usecomputing resources in a “cloud” network cluster for the imagereconstructor 230.

In one embodiment, the image reconstructor 230 stores the imagesreconstructed in the storage device 218. Alternatively, the imagereconstructor 230 may transmit the reconstructed images to the computingdevice 216 for generating useful patient information for diagnosis andevaluation. In certain embodiments, the computing device 216 maytransmit the reconstructed images and/or the patient information to adisplay or display device 232 communicatively coupled to the computingdevice 216 and/or the image reconstructor 230. In some embodiments, thereconstructed images may be transmitted from the computing device 216 orthe image reconstructor 230 to the storage device 218 for short-term orlong-term storage.

The various methods and processes (such as the method described belowwith reference to FIG. 3 ) described further herein may be stored asexecutable instructions in non-transitory memory on a computing device(or controller) in imaging system 200. In one embodiment, imagereconstructor 230 may include such executable instructions innon-transitory memory, and may apply the methods described herein toreconstruct an image from scanning data. In another embodiment,computing device 216 may include the instructions in non-transitorymemory, and may apply the methods described herein, at least in part, toa reconstructed image after receiving the reconstructed image from imagereconstructor 230. In yet another embodiment, the methods and processesdescribed herein may be distributed across image reconstructor 230 andcomputing device 216.

In one embodiment, the display 232 allows the operator to evaluate theimaged anatomy. The display 232 may also allow the operator to select avolume of interest (VOI) and/or request patient information, forexample, via a graphical user interface (GUI) for a subsequent scan orprocessing.

Though a CT system is described by way of example, it should beunderstood that the present techniques may also be useful when appliedto images acquired using other imaging modalities, such astomosynthesis, positron emission tomography (PET), single-photonemission computed tomography (SPECT), C-arm angiography, and so forth.The present discussion of a CT imaging modality is provided merely as anexample of one suitable imaging modality.

FIG. 3 is a flow chart illustrating a method 300 for metal artifactreduction that addresses metal artifacts during image reconstructionwhile preserving image resolution, according to an embodiment of thedisclosure. Method 300 is described with regard to the systems andcomponents of FIGS. 1-2 , though it should be appreciated that themethod 300 may be implemented with other systems and components withoutdeparting from the scope of the present disclosure. Method 300 may becarried out according to instructions stored in non-transitory memory ofan image processing system, such as computing device 216 and/or imagereconstructor 230 of FIG. 2 . Method 300 is described below with respectto mitigating artifacts due to the presence of metal. However, it is tobe understood that the embodiments described herein (e.g., removal ofcorrupted projection data and interpolation over the removed data withthe addition of high frequency content) may be performed to mitigateartifacts due to the presence of other dense objects, such as a densebolus of contrast agent.

At 302, projection data of an imaging subject is obtained. Theprojection data includes x-ray radiation attenuation measurementsobtained from a detector array of a CT imaging system (e.g., detector108 of CT imaging system 100). The projection data may include one ormore views, with each view including the projection data obtained at onegantry or view angle. At 304, method 300 includes determining if one ormore metal (or other high-density) regions are present in the originalprojection data (referred to as P_(O)). As used herein, the term “metal”is used to denote objects or pixels/voxels in an image corresponding tohigh x-ray attenuation properties, even if those objects are not metal.The presence of the one or more metal regions may be determined byreconstructing one or more images from the original projection data andidentifying the presence or absence of metal in each reconstructedimage. The metal may be identified based on image processing techniquesby analyzing pixel or voxel intensity of one or more regions of thereconstructed images. For example, a metal mask may be generated whereineach pixel/voxel that has an intensity that is greater than a thresholdis included in the mask, and one or more metal regions may be identifiedbased on the mask. Other mechanisms for determining the presence orabsence of metal are possible, such as machine learning techniques, userinput (e.g., a user may enter input identifying which pixels of an imageare part of a metal artifact), etc. For identifying the metal, astandardized reconstruction may be used looking at the full field ofview to help ensure all metal objects are identified even if they areoutside a user prescribed targeted reconstruction.

FIG. 4 shows an example sequence of sinogram images 400 that representvarious aspects of the metal artifact reduction method described hereinwith respect to FIG. 3 . The sequence of sinogram images 400 includes afirst sinogram image 402. The first sinogram image 402 represents theoriginal projection data. As appreciated by first sinogram image 402,metal 410 is present in the original projection data (as also shown bythe arrows pointing to high intensity regions of first sinogram image402).

If no metal is detected, method 300 proceeds to 306 to reconstruct oneor more images from the projection data without applying a metalartifact reduction method. For example, one or more diagnostic imagesmay be reconstructed using known reconstruction techniques, such asfiltered back projection or iterative reconstruction. The reconstructionperformed for generating the diagnostic images may be the samereconstruction process as used to identify the metal, or a differentreconstruction process. For example, the user may reconstruct CT imagedata with various techniques, such as generating a higher resolutionimage by using a different filter back projection kernel (reconstructionkernel) or by targeting a certain anatomy and adjusting the field ofview. The images may be reconstructed based on data acquired from eachview. At 322, the one or more diagnostic images may be output to adisplay device (e.g., display device 232) for display to an operator ora physician, to a storage medium (e.g., mass storage 218) for retrievingat a later time, and so on. Method 300 may then return.

Returning to 304, if one or more metal regions are detected, method 300proceeds to 308 to identify metal-containing region(s) in the originalprojection data P_(O). For example, a region of metal or otherhigh-density material is identified in a reconstructed image, asexplained above. The identified region in the reconstructed image ismapped back to the original projection data, and every component of theprojection data determined to include/be corrupted by metal isidentified as the metal-containing region(s) of the original projectiondata. For example, each detector element of the detector array that isdetermined to output projection data in a metal-containing region or becorrupted by the metal may be identified, and the projection data fromthose detector elements may be identified as the metal-containingregion(s). In this way, metal may be identified in the image spacedomain from a standardized reconstruction. CT numbers may be used todetermine which image pixels contain metal. The metal is then mappedback to the projection space domain and can be segmented andinterpolated, as described below.

At 310, the metal-containing region(s) of the projection data areinterpolated in order to remove the projection data in themetal-containing region(s) and replace the projection data in themetal-containing region(s) with interpolated, non-corrupted data,thereby generating interpolated projection data (P_(I)). Theinterpolation may include a weighted interpolation where projection datafrom neighboring, non-metal containing detector elements is used toestimate projection data from the metal-containing detector elements.For example, the interpolation may include interpolating in row,channel, and view directions using valid neighbors of a metal-containingdetector element, assigning weights to each of the neighbors, andreplacing the data of the metal-containing detector element by the sumof the weighted neighbors. In addition, previously interpolatedprojection data can be used when interpolating in the view direction. Anexample of interpolated projection data is shown as second sinogramimage 404 of FIG. 4 . Upon identifying the metal-containing regions(e.g., metal 410) in the first sinogram image 402 of FIG. 4 , themetal-containing regions are interpolated as described above to generateinterpolated regions 412. The arrows in the second sinogram image 404also point to the interpolated regions, where the metal-corruptedprojection data is interpolated.

At 312, the interpolated regions from the original (non-interpolated)projection data are segmented, thereby extracting the originalprojection data in the interpolated/metal-containing regions. FIG. 4shows an example segmented sinogram image 406 representing the regions414 of the projection data that are segmented. In the segmented sinogramimage 406, the white pixels represent the interpolated regions and thegray pixels represent the non-interpolated regions. The identifiedprojection data is extracted while the remaining original projectiondata is discarded (for the purpose of the extraction of the highfrequency content information described below) to generate an originalsegmented view (e.g., the original projection data of only theinterpolated/metal-containing regions).

At 314, high frequency content information from the segmentedinterpolated regions of the projection data P_(O) is extracted. The highfrequency content information may include a subset of all the frequencycontent information of the original projection data within theinterpolated regions, such as an upper portion (e.g., upper half) of allthe frequency content information. To extract the high frequency contentinformation, a 2-D filter or 3-D filter (e.g., channel, row, views) maybe applied to the original segmented view to smooth the data out, andthe resultant smoothed segmented view may be subtracted from theoriginal segmented view to extract the high frequency content. Thefilter may be a suitable filter, such as a Gaussian filter (in the viewdomain and/or frequency domain in each view). For example, theprojection data of the original segmented view may be transformed to thefrequency domain (e.g., via a Fourier transform), and the filter may bea low-pass filter that has a cut off that is in the middle of thefrequency space. The remaining frequency content (e.g., the lowfrequency content) may be subtracted from the frequency content of theoriginal segmented view to extract only the high frequency contentinformation. Extracting the high frequency content information mayinclude excluding at least some lower frequency content information(e.g., a lower portion/half of all the frequency content information ofthe projection data within the interpolated regions).

Any of the higher frequency content (anything above DC) may be useful inretaining some amount of resolution. The specific filter applied may beselected based on the acquisition system and the desired results,trading off image noise, resolution, and artifact. Further, the filterthat is applied may be selected based on the clinical task. For example,for high resolution scanning, such as muscular skeletal, increasedresolution may be more important than the negative impact of noiseincrease; whereas in non-contrast head imaging, noise may be a biggerconcern than resolution. The filtration may also be selected so as notto introduce CT number shifts in the final image. One example of filterthat may be used is a 3×3 boxcar filter [ 1/9 1/9 1/9; 1/9 1/9 1/9; 1/91/9 1/9], however a more complicated filter could be used to extract thedesired frequencies to achieve the desired end results.

At 316, the high frequency content information is conditioned.Conditioning the high frequency content information may includeperforming thresholding and/or morphological operations, such asweighting the high frequency content, thresholding the high frequencycontent, and/or filtering the high frequency content information, inorder to remove high frequency content information of the originalprojection data that may cause artifacts in the final images, such asstreaking. In one example, the conditioning may include applying aweighting to reduce the contribution of the high frequency contentinformation at the edges of the metal. In another example, theconditioning may include thresholding where the high frequency contentinformation having a projection intensity value over a threshold isdiscarded or clipped to a value such as the threshold value. Anotherexample of the conditioning may include further frequency filtration totarget and reduce a specific frequency/frequencies that is known tocause artifacts in the resulting image. In some examples, more than oneconditioning process may be applied to the high frequency contentinformation, such as weighting and thresholding the high frequencycontent information. In some examples, different conditioning processesmay be applied for different types of metal artifacts, differentanatomical features, or other factors. For example, a first conditioningprocess may be applied when the metal artifact is relatively small(e.g., a dental filling) and a second, different conditioning processmay be applied when the metal artifact is relatively large (e.g., aprosthetic knee).

At 318, the high frequency content information (e.g., the conditionedhigh frequency content information) is added back to the interpolatedprojection data P_(I) to generate adjusted projection data (P_(A)). Forexample, the extracted, conditioned high frequency content informationis added back to the interpolated projection data that was interpolatedat 310. The high frequency content information may be simply added tothe interpolated projection data, or the high frequency contentinformation may be adaptively added, where the high frequency contentinformation is weighted, blended, etc., with the interpolated projectiondata. An example sinogram image showing adjusted projection data isshown at final sinogram image 408 of FIG. 4 . Final sinogram image 408is generated by adding the high frequency content information from thesegmented sinogram image 406 to the interpolated, second sinogram image404. In this way, the metal artifacts may be identified and interpolatedover, such as in region 416. However, the high frequency contentinformation that may be lost in the interpolation process may beextracted from the original projection data and added back in to theinterpolated projection data. It is to be understood that theinterpolation, segmentation, high frequency content extraction, andaddition of the high frequency content information to the interpolatedprojection data may be performed for each view of the projection data inwhich metal-containing detector elements are detected.

At 320, one or more images are reconstructed from the adjustedprojection data P_(A). The image reconstruction may use filtered backprojection, iterative reconstruction, or another suitable reconstructiontechnique. The images may be reconstructed based on data acquired fromeach view. At 322, the one or more diagnostic images may be output to adisplay device (e.g., display device 232) for display to an operator ora physician, to a storage medium (e.g., mass storage 218) for retrievingat a later time, and so on. Method 300 may then return.

In some examples, some or all of the above-described metal artifactreduction method may be performed using machine learning, such as one ormore deep learning models. For example, the identification of themetal-containing regions in the projection data and/or interpolation ofthe metal-containing regions may be performed by a deep learning model.Additionally or alternatively, the extraction of the high frequencycontent information and/or conditioning of the extracted high frequencycontent information may be performed by one or more deep learningmodels. For example, a deep learning model may be trained to apply afilter or otherwise extract selected frequency content (e.g., the highfrequency content information described herein) from segmentedprojection data. Additionally or alternatively, a deep learning modelmay be trained to identify which conditioning process (from among aplurality of different conditioning processes) should be applied to theextracted high frequency content information in order to optimallycondition the high frequency content information. Such a deep learningmodel may be trained with a plurality of training datasets, with eachtraining dataset including a conditioning process and correspondingindication of a relative level of success (e.g., as determined by anexpert) that the conditioning process maintained image resolutionwithout introducing residual artifacts. In some examples, theconditioning itself may be performed by the deep learning model. Forexample, the deep learning model may be trained to apply a weightingscheme and/or thresholding to the high frequency content information.The training may be based on image datasets with the undesired input andthe desired output for the conditioning step. Generating the imagedatasets may be a manual process where an expert looks at eachindividual image set and optimizes the conditioning for that exam sothat the deep learning model applies the optimal conditioning for eachspecific CT scan.

FIG. 5 shows a set of images 500 illustrating metal artifacts,conventional metal artifact reduction, and the metal artifact reductionof the present disclosure. The set of images 500 includes a first column510 of images, a second column 520 of images, and a third column 530 ofimages. Each image in the first column 510 shows a different section ofan anatomical feature of a patient having a non-corrected metalartifact(s), each image in the second column 520 shows a firstcorrection to the corresponding metal artifact where the high frequencycontent information is not included in the correction, and each image inthe third column 530 shows a second correction to the correspondingmetal artifact where the high frequency content information is addedback during the correction. The images in the second column 520 areimages reconstructed from the same projection data as the images in thefirst column 510 (e.g., showing the axial, coronal, and sagittal viewsof the knee of the patient), but with the application of theconventional metal artifact reduction. The images in the third column530 are images reconstructed from the same projection data as the imagesin the first column 510 (e.g., showing the axial, coronal, and sagittalviews of the knee of the patient), but with the application of the metalartifact reduction of the present disclosure.

Referring to the first row of images, a first image 502 is an axial viewof a knee of a patient, where the patient has a knee implant (e.g., kneereplacement) that includes multiple pieces of metal. Without performingany metal artifact reduction, patient anatomy near the metal isdifficult to visualize, and the metal causes various artifacts,including streaking, aliasing, shadowing, saturation of pixels near themetal, etc. As an example, the arrow in the first image 502 is pointingto a streaking/shadowing artifact caused by the metal, where anatomicalfeatures away from the metal are not visible.

A second image 504 shows the axial view of the knee of the patient shownin the first image 502, but with a metal artifact reduction methodapplied to reduce the effects of the metal. The metal artifact reductionthat is applied to generate the second image 504 may include identifyingthe metal-containing regions in the projection data and interpolatingthose regions. While the interpolation reduces the effects of the metaland allows adjacent anatomical features to be better visualized (e.g.,relative to the first image 502), artifacts may still be present, andloss of resolution may occur due to the interpolation. For example, thearrow in the second image 504 is pointing to a region ofstreaking/shadowing caused by the metal. Other issues are also visiblein the second image 504, such as low resolution in the center of theknee and low resolution around the edges of the metal.

A third image 506 shows the axial view of the knee of the patient shownin the first image 502, but with the metal artifact reduction method ofthe present disclosure applied to reduce the effects of the metal. Themetal artifact reduction that is applied to generate the third image 506may include identifying the metal-containing regions in the projectiondata, interpolating those regions, extracting/conditioning the highfrequency content information of the original projection data in theinterpolated regions, and adding the conditioned high frequency contentinformation back to the interpolated projection data. In one example,the third image 506 may be generated by applying a 2D filter to extractthe high frequency content and conditioning the high frequency contentvia an adaptive weighting with a thresholding to clip extreme values,before adding the conditioned frequency content back to the interpolatedprojection. As appreciated by the arrow in the third image 506, thestreaking/shadowing present in the second image 506 has been addressed,and the anatomical features in the region of the arrow are visible. Theresolution of the center of the knee is increased in the third image 506relative to the second image 504, and the anatomical features at theedge of the metal are visible. Thus, the inclusion of the high frequencycontent information in the interpolated projection data results inreconstructed images of higher resolution and having fewer/less severeartifacts.

A fourth image 508, a fifth image 512, and a sixth image 514 each show acoronal section of the knee of the patient including themetal-containing knee implant, without metal artifact reduction (fourthimage 508), with conventional metal artifact reduction (fifth image512), and with the metal artifact reduction of the present disclosure(sixth image 514). The arrows in the fifth image 512 point to some ofthe low-resolution regions that are corrected/improved in the sixthimage 514.

A seventh image 516, an eighth image 518, and a ninth image 522 eachshow a sagittal section of the knee of the patient including themetal-containing knee implant, without metal artifact reduction (seventhimage 516), with conventional metal artifact reduction (eighth image518), and with the metal artifact reduction of the present disclosure(ninth image 522). The arrows in the eighth image 518 point to some ofthe low-resolution regions that are corrected/improved in the ninthimage 522.

FIG. 6 shows a set of images 600 including a first column 610 of images,a second column 620 of images, and a third column 630 of images. Theimages in the first column 610 include an axial section, a coronalsection, and a sagittal section of a spine of a patient including metalscrews. The images of the first column 610 are uncorrected and thusdisplay high levels of artifacts due to the presence of the metalscrews. The images in the second column 620 are images reconstructedfrom the same projection data as the images in the first column 610(e.g., showing the axial, coronal, and sagittal views of the spine ofthe patient), but with the application of the conventional metalartifact reduction. The images in the third column 630 are imagesreconstructed from the same projection data as the images in the firstcolumn 610 (e.g., showing the axial, coronal, and sagittal views of thespine of the patient), but with the application of the metal artifactreduction of the present disclosure. The arrows in the images of thethird column 630 point to regions where image resolution was improvedrelative to the corresponding image of the second column 620 and/orwhere various artifacts were reduced/resolved, such as streaking orshadowing.

The technical effect of adding in high frequency content information tointerpolated regions of projection data and then reconstructing imagesfrom the projection data is that metal artifacts may be reduced whilemaintaining image resolution.

In another representation, a method includes reconstructing one or morediagnostic images from an adjusted projection dataset, the adjustedprojection dataset including a metal-containing region comprising highfrequency content information extracted from an original projectiondataset at the metal-containing region and interpolated projection data.

An example provides for a method including obtaining projection data ofan imaging subject; identifying a metal-containing region in theprojection data; interpolating the metal-containing region to generateinterpolated projection data; extracting high frequency contentinformation from the projection data in the metal-containing region;adding the extracted high frequency content information to theinterpolated projection data to generate adjusted projection data; andreconstructing one or more diagnostic images from the adjustedprojection data. In a first example of the method, adding the extractedhigh frequency content information to the interpolated projection datacomprises conditioning the extracted high frequency content informationand adding the conditioned high frequency content information to theinterpolated projection data. In a second example of the method, whichoptionally includes the first example, conditioning the extracted highfrequency content information comprises weighting and/or thresholdingthe high frequency content information. In a third example of themethod, which optionally includes one or both of the first and secondexamples, conditioning the extracted high frequency content informationcomprises conditioning the high frequency content information via a deeplearning model. In a fourth example of the method, which optionallyincludes one or more or each of the first through third examples,extracting the high frequency content information comprises segmentingthe metal-containing region of the projection data, transforming thesegmented projection data to the frequency domain, and applying a filterto the segmented projection data in the frequency domain to extract thehigh frequency content information. In a fifth example of the method,which optionally includes one or more or each of the first throughfourth examples, the filter is a low-pass filter and wherein applyingthe low-pass filter to the segmented projection data to extract the highfrequency content information comprises filtering out the high frequencycontent information via the low-pass filter and subtracting the filteredsegmented projection data from the segmented projection data to extractthe high frequency content information. In a sixth example of themethod, which optionally includes one or more or each of the firstthrough fifth examples, the low-pass filter has a cut-off in a middle ofthe frequency content of the projection data in the frequency domain. Ina seventh example of the method, which optionally includes one or moreor each of the first through sixth examples, extracting the highfrequency content information comprises extracting the high frequencycontent information via a deep learning model. In an eighth example ofthe method, which optionally includes one or more or each of the firstthrough seventh examples, identifying the metal-containing region in theprojection data comprises reconstructing one or more initial images fromthe projection data, identifying one or more pixels of the one or moreinitial images having an intensity greater than a threshold intensity,and mapping the identified one or more pixels back to the projectiondata.

An example provides for a method, including removing projection datafrom a metal-containing region of an original projection dataset,including removing all frequency content of the projection data in themetal-containing region; replacing the removed projection data in themetal-containing region with interpolated projection data; adding back asubset of the frequency content of the removed projection data to theinterpolated projection data to generate an adjusted projection dataset;and reconstructing one or more images from the adjusted projectiondataset. In a first example of the method, the subset of the frequencycontent comprises an upper portion of the frequency content, and furthercomprising excluding at least some of a lower portion of the frequencycontent from the adjusted projection dataset. In a second example of themethod, which optionally includes the first example, the method furtherincludes conditioning the subset of the frequency content before addingthe subset of the frequency content back to the interpolated projectiondata. In a third example of the method, which optionally includes one orboth of the first and second examples, conditioning the subset of thefrequency content comprises conditioning the subset of the frequencycontent via thresholding and/or morphological operations. In a fourthexample of the method, which optionally includes one or more or each ofthe first through third examples, conditioning the subset of thefrequency content comprises conditioning the subset of the frequencycontent via a deep learning model.

An example of an image processing system includes a processor; and anon-transitory memory storing instructions executable by the processorto: interpolate a metal-containing region of a projection dataset of animaging subject to generate an interpolated projection dataset; extracthigh frequency content information from the projection dataset in themetal-containing region; condition the high frequency contentinformation; add the conditioned high frequency content information tothe interpolated projection dataset to generate an adjusted projectiondataset; and reconstruct one or more diagnostic images from the adjustedprojection dataset. In a first example of the system, the non-transitorymemory stores one or more deep learning models configured to extract thehigh frequency content information from the projection dataset in themetal-containing region and/or condition the high frequency contentinformation. In a second example of the system, which optionallyincludes the first example, the high frequency content comprises asubset of all frequency content of the projection dataset in themetal-containing region. In a third example of the system, whichoptionally includes one or both of the first and second examples, theinstructions to condition the high frequency content informationcomprise instructions to weight and/or threshold the high frequencycontent information. In a fourth example of the system, which optionallyincludes one or more or each of the first through third examples, theinstructions are executable to identify the metal-containing region ofthe projection dataset by reconstructing an initial image from theprojection dataset, generating a metal mask that includes eachmetal-containing pixel of the initial image, and map the metal mask backto the projection dataset. In a fifth example of the system, whichoptionally includes one or more or each of the first through fourthexamples, the projection dataset is acquired by a computed tomography(CT) imaging system.

As used herein, an element or step recited in the singular and proceededwith the word “a” or “an” should be understood as not excluding pluralof said elements or steps, unless such exclusion is explicitly stated.Furthermore, references to “one embodiment” of the present invention arenot intended to be interpreted as excluding the existence of additionalembodiments that also incorporate the recited features. Moreover, unlessexplicitly stated to the contrary, embodiments “comprising,”“including,” or “having” an element or a plurality of elements having aparticular property may include additional such elements not having thatproperty. The terms “including” and “in which” are used as theplain-language equivalents of the respective terms “comprising” and“wherein.” Moreover, the terms “first,” “second,” and “third,” etc. areused merely as labels, and are not intended to impose numericalrequirements or a particular positional order on their objects.

This written description uses examples to disclose the invention,including the best mode, and also to enable a person of ordinary skillin the relevant art to practice the invention, including making andusing any devices or systems and performing any incorporated methods.The patentable scope of the invention is defined by the claims, and mayinclude other examples that occur to those of ordinary skill in the art.Such other examples are intended to be within the scope of the claims ifthey have structural elements that do not differ from the literallanguage of the claims, or if they include equivalent structuralelements with insubstantial differences from the literal languages ofthe claims.

The invention claimed is:
 1. A method, comprising: obtaining original projection data of an imaging subject; identifying a metal-containing region in the original projection data; interpolating the metal-containing region to generate interpolated projection data, wherein the interpolated projection data is projection data; extracting high frequency projection data from the original projection data in the metal-containing region, including segmenting the metal-containing region of the original projection data, transforming the segmented projection data to the frequency domain, and applying a filter to the segmented projection data in the frequency domain; conditioning the high frequency projection data via a deep learning model trained to select a conditioning process from among a plurality of different conditioning processes to be applied and apply the selected conditioning process to the high frequency projection data; adding the conditioned extracted high frequency projection data to the interpolated projection data to generate adjusted projection data; and reconstructing one or more diagnostic images from the adjusted projection data.
 2. The method of claim 1, wherein conditioning the extracted high frequency projection data comprises weighting and thresholding the high frequency projection data.
 3. The method of claim 1, wherein the filter is a first filter and wherein conditioning the extracted high frequency projection data comprises applying a second filter to the extracted high frequency projection data, the second filter configured to filter one or more frequencies known to cause image artifacts.
 4. The method of claim 1, wherein the filter is a low-pass filter.
 5. The method of claim 4, wherein the low-pass filter has a cut-off in a middle of the frequency content of the projection data in the frequency domain.
 6. The method of claim 1, wherein transforming the segmented projection data to the frequency domain comprises transforming the segmented projection data to the frequency domain via a Fourier transform.
 7. The method of claim 1, wherein identifying the metal-containing region in the original projection data comprises reconstructing one or more initial images from the projection data, identifying one or more pixels of the one or more initial images having an intensity greater than a threshold intensity, and mapping the identified one or more pixels back to the original projection data, such that every component of the original projection data determined to be corrupted by metal is identified as the metal-containing region of the original projection data.
 8. A method, comprising: removing projection data from a metal-containing region of an original projection dataset, including removing all frequency content of the projection data in the metal-containing region; replacing the removed projection data in the metal-containing region with interpolated projection data; conditioning a subset of the frequency content of the removed projection data via a deep learning model trained to select a conditioning process from among a plurality of different conditioning processes and apply the selected conditioning process to the subset of the frequency content; adding back the conditioned subset of the frequency content of the removed projection data to the interpolated projection data to generate an adjusted projection dataset, wherein the interpolated projection data is projection data; and reconstructing one or more images from the adjusted projection dataset using an iterative or analytic image reconstruction technique.
 9. The method of claim 8, wherein the subset of the frequency content comprises a high frequency content subset of the frequency content, the high frequency content subset including frequency content that has a higher frequency than a remaining subset of the frequency content, and further comprising excluding at least some of the remaining subset of the frequency content from the adjusted projection dataset.
 10. The method of claim 8, wherein the deep learning model is trained with a plurality of training datasets, each training dataset including a conditioning process and a corresponding indication of a relative level of success as determined by an expert that the conditioning process maintained image resolution without introducing residual artifacts.
 11. An image processing system, comprising: a processor; and a non-transitory memory storing instructions executable by the processor to: interpolate a metal-containing region of a projection dataset of an imaging subject to generate an interpolated projection dataset, wherein the interpolated projection dataset comprises projection data; extract high frequency content information from the projection dataset in the metal-containing region; condition the high frequency content information by thresholding the high frequency content information, the thresholding including discarding or clipping high frequency content information having a projection intensity value over a threshold; add the conditioned high frequency content information to the interpolated projection dataset to generate an adjusted projection dataset; and reconstruct one or more diagnostic images from the adjusted projection dataset.
 12. The image processing system of claim 11, wherein the non-transitory memory stores one or more deep learning models configured to extract the high frequency content information from the projection dataset in the metal-containing region and/or condition the high frequency content information.
 13. The image processing system of claim 11, wherein the high frequency content comprises a subset of all frequency content of the projection dataset in the metal-containing region.
 14. The image processing system of claim 11, wherein the instructions are executable to identify the metal-containing region of the projection dataset by reconstructing an initial image from the projection dataset, generating a metal mask that includes each metal-containing pixel of the initial image, and mapping the metal mask back to the projection dataset.
 15. The image processing system of claim 11, wherein the projection dataset is acquired by a computed tomography (CT) imaging system and includes radiation attenuation measurements detected by a plurality of detector elements, wherein reconstructing the one or more diagnostic images from the adjusted projection dataset comprises using filtered backprojection to reconstruct the one or more diagnostic images, and wherein interpolating the metal-containing region of the projection dataset includes identifying, in row, channel, and view directions, valid neighbors of each metal-containing detector element, assigning weights to each of the neighbors, and replacing projection data of each metal-containing detector element by a sum of its weighted neighbors. 